Bähner F et al., "Abstract rule learning promotes cognitive flexibility in complex environments across species"

Data set and code for Bähner F et al., "Abstract rule learning promotes cognitive flexibility in complex environments across species" -Behavioral (response data, video tracking data in rats) and electrophysiological data (multiple single-unit recordings, MEG) in a multidimensional rule learning task in rats and humans -Matlab Code: strategy detection algorithm and RL models, helper files to create figures or to prepare data for use of other toolboxes used in this study (FieldTrip: https://www.fieldtriptoolbox.org/, MVPA-Light toolbox: https://github.com/treder/MVPA-Light and Neural Decoding Toolbox: www.readout.info)本数据集与配套代码源自Bähner F等人的学术论文《抽象规则学习促进跨物种复杂环境中的认知灵活性》。 数据集包含大鼠与人类在多维规则学习任务中的相关数据：其中行为学数据包括反应数据与大鼠行为视频追踪数据，电生理数据包括多单元单电极记录数据与脑磁图（MEG）数据。 配套Matlab代码涵盖策略检测算法、强化学习（Reinforcement Learning，RL）模型，以及用于生成实验图表或预处理数据以适配本研究所用工具包的辅助文件。本研究使用的工具包包括：FieldTrip（https://www.fieldtriptoolbox.org/）、MVPA-Light工具包（https://github.com/treder/MVPA-Light）以及神经解码工具包（Neural Decoding Toolbox，www.readout.info）